Bistable Stopper Design and Force Prediction for Precision and Power Grasps of Soft Robotic Fingers for Industrial Manipulation

Abstract This paper aims at presenting a detailed and practical comparison between three designs of robotic soft fingers for industrial grippers. While the soft finger based on the Fin Ray Effect (FRE) has been proposed for quite some time, few works in the literature have studied its reliance on the presence of the crossbeams or its precision grasp performance compared to its power grasp. Aiming at addressing these gaps, two novel designs are proposed and compared to the classic FRE fingers in this paper. First, the three designs are presented and one of the fingers, PacomeFlex, embeds changeable grasping modes by relying on two sets of kinematic structures of a single bistable stopper design. Then, finite element analyses are conducted to simulate their power and precision grasps followed by the estimation of the overall grasp forces they produce. These finite element analyses will then be used to train neural networks capable of predicting the grasp forces produced by the fingers. Finally, the grasp strength and the pullout resistance of the fingers are experimentally measured and experimental results are shown to be in good accordance with the FEA and neural network models. As will also be shown, the PacomeFlex finger introduced in this work provides a noticeably higher performance level than Festo's commercial product with respect to typical metrics in soft grasping.